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FaceFusion and DeepFaceLab lead in overall output quality, while Roop and FaceFusion offer the best balance of performance and ease of use.

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| Factor | Why It Matters | Best Practice | |--------|----------------|----------------| | | The neural network extracts features more accurately from high‑resolution, well‑lit photos. | Use a 1024×1024 pixel or higher front‑facing photo with even lighting and no obstructions (glasses, hats, heavy shadows). | | Target video resolution | Low‑resolution footage loses fine detail after the swap, making the face look blurry or pixelated. | Aim for at least 720p; 1080p or higher is recommended. | | Face angles | Side profiles (angles beyond 45 degrees) often confuse detection algorithms, leading to distorted swaps. | Prefer front‑facing or slightly turned angles. If side profiles are needed, test the tool thoroughly and consider manual alignment. | | Occlusions | Hands, hair, glasses, or other objects can break the face mask and cause obvious seams. | Use the tool’s occluder mask feature if available. For best results, choose footage where the face is largely unobstructed. | | Lighting consistency | A swapped face retains the lighting from the source photo. If the target video has different lighting, the result can look pasted on. | Edit the source and target to have similar color tones, or use a post‑processing tool to match lighting. | or similar devices

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